Project Introduction:
Automatic recognition and vectorization of remote sensing images have significant application value in fields such as geological exploration, transportation planning, land and forestry resource utilization, and geological disaster detection and prevention. The project team employs deep learning techniques to achieve high-precision segmentation of target objects in remote sensing images. Additionally, they use related graphical technologies to vectorize and stitch the segmented mask images.
Application Areas:
Field construction
Road planning
Resource assessment
Geological disaster monitoring
Technical Advantages:
The use of deep learning for remote sensing image segmentation ensures high accuracy, with results closely matching manual interpretation.
The use of tile-based recognition methods provides high efficiency, and the combination of graphical vectorization and stitching techniques enables the recognition and vectorization of large-scale remote sensing images.
